{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b7f2553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPENAI_API_KEY: ········\n"
     ]
    }
   ],
   "source": [
    "import os, getpass\n",
    "\n",
    "def _set_env(var: str):\n",
    "    if not os.environ.get(var):\n",
    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
    "\n",
    "_set_env(\"OPENAI_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb7c0651",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPENAI_BASE_URL: ········\n"
     ]
    }
   ],
   "source": [
    "_set_env(\"OPENAI_BASE_URL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fa5492a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LANGCHAIN_API_KEY: ········\n"
     ]
    }
   ],
   "source": [
    "_set_env(\"LANGCHAIN_API_KEY\")\n",
    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "os.environ[\"LANGCHAIN_PROJECT\"] = \"react-agent\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b9e62e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "\n",
    "#乘法工具\n",
    "def multiply(a: int, b: int) -> int:\n",
    "    \"\"\"将a和b相乘.\n",
    "\n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a * b\n",
    "\n",
    "# 加法工具\n",
    "def add(a: int, b: int) -> int:\n",
    "    \"\"\"将a和b相加.\n",
    "\n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a + b\n",
    "\n",
    "# 除法工具\n",
    "def divide(a: int, b: int) -> float:\n",
    "    \"\"\"将a和b相除.\n",
    "\n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a / b\n",
    "\n",
    "tools = [add, multiply, divide]\n",
    "llm = ChatOpenAI(model=\"gpt-4o\")\n",
    "\n",
    "# 对于这个案例，我们将并行工具调用设置为 false，因为数学运算通常是按顺序完成的，而这次我们有 3 个可以进行数学运算的工具\n",
    "# OpenAI 模型专门默认使用并行工具调用来提高效率，请参阅 https://python.langchain.com/docs/how_to/tool_calling_parallel/\n",
    "llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a64a354",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.graph import MessagesState\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "\n",
    "# 系统提示词\n",
    "sys_msg = SystemMessage(content=\"您是一位乐于助人的助手，负责对一组输入执行算术运算。\")\n",
    "\n",
    "# 节点\n",
    "def assistant(state: MessagesState):\n",
    "   return {\"messages\": [llm_with_tools.invoke([sys_msg] + state[\"messages\"])]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88113cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langgraph.graph import START, StateGraph\n",
    "from langgraph.prebuilt import tools_condition\n",
    "from langgraph.prebuilt import ToolNode\n",
    "from IPython.display import Image, display\n",
    "\n",
    "# 初始化图\n",
    "builder = StateGraph(MessagesState)\n",
    "\n",
    "# 定义节点：这些节点完成工作\n",
    "builder.add_node(\"assistant\", assistant)\n",
    "builder.add_node(\"tools\", ToolNode(tools))\n",
    "\n",
    "# 定义边：这些决定了控制流如何移动\n",
    "builder.add_edge(START, \"assistant\")\n",
    "builder.add_conditional_edges(\n",
    "    \"assistant\",\n",
    "    # 如果助手的最新消息（结果）是工具调用 -> tools_condition 路由到工具节点\n",
    "    # 如果来自助手的最新消息（结果）不是工具调用 -> tools_condition 路由到终点\n",
    "    tools_condition,\n",
    ")\n",
    "# 循环工具到LLM的边\n",
    "builder.add_edge(\"tools\", \"assistant\")\n",
    "react_graph = builder.compile()\n",
    "\n",
    "\n",
    "display(Image(react_graph.get_graph(xray=True).draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbdc7e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [HumanMessage(content=\"将 3 和 4 相加。将输出乘以 2。再将输出除以 5\")]\n",
    "messages = react_graph.invoke({\"messages\": messages})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3121cfb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "将 3 和 4 相加。将输出乘以 2。再将输出除以 5\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  add (call_IQwQj31vxZXVyvkHzxRWn84O)\n",
      " Call ID: call_IQwQj31vxZXVyvkHzxRWn84O\n",
      "  Args:\n",
      "    a: 3\n",
      "    b: 4\n",
      "  multiply (call_fUzS2gfdrB7n5MqG6FXpcTvg)\n",
      " Call ID: call_fUzS2gfdrB7n5MqG6FXpcTvg\n",
      "  Args:\n",
      "    a: 7\n",
      "    b: 2\n",
      "  divide (call_yQ3TzzWtGEGYfW4mgdA8MC2K)\n",
      " Call ID: call_yQ3TzzWtGEGYfW4mgdA8MC2K\n",
      "  Args:\n",
      "    a: 14\n",
      "    b: 5\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: add\n",
      "\n",
      "7\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: multiply\n",
      "\n",
      "14\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: divide\n",
      "\n",
      "2.8\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "结果如下：\n",
      "1. 3 和 4 相加得到 7。\n",
      "2. 将 7 乘以 2 得到 14。\n",
      "3. 最后将 14 除以 5 得到 2.8。\n"
     ]
    }
   ],
   "source": [
    "for m in messages['messages']:\n",
    "    m.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38404042",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10",
   "language": "python",
   "name": "python310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
